{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/NielsRogge/Transformers-Tutorials/blob/master/Pix2Struct/Fine_tune_Pix2Struct_on_key_value_pair_dataset_(PyTorch_Lightning).ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g-omEyoV-DJm"
      },
      "source": [
        "# Fine-tune Pix2Struct on a key-value pair dataset\n",
        "\n",
        "In this notebook, we'll fine-tune Google's [Pix2Struct](https://huggingface.co/docs/transformers/main/en/model_doc/pix2struct) model on the [CORD dataset](https://huggingface.co/datasets/naver-clova-ix/cord-v2), in the format in which the Donut authors (Donut is a model very similar to Pix2Struct in terms of architecture) prepared it. See also my [notebook](https://github.com/NielsRogge/Transformers-Tutorials/blob/master/Donut/DocVQA/Creating_a_toy_DocVQA_dataset_for_Donut.ipynb) regarding preparing a custom dataset in this format.\n",
        "\n",
        "The goal for the model is to predict a piece of text given a document image.\n",
        "\n",
        "## Set-up environment\n",
        "\n",
        "We start by installing 🤗 Transformers and 🤗 Datasets."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "w8m7YDPbfxtV",
        "outputId": "5437720f-a8f0-4ba9-f254-2879397d5e0b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.6/7.6 MB\u001b[0m \u001b[31m23.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m199.8/199.8 KB\u001b[0m \u001b[31m23.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Building wheel for transformers (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n"
          ]
        }
      ],
      "source": [
        "!pip install -q git+https://github.com/huggingface/transformers.git"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qriqrURi93k_",
        "outputId": "b8564f9c-9d17-4c69-bd3e-eceef756acb8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m468.7/468.7 KB\u001b[0m \u001b[31m22.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.0/1.0 MB\u001b[0m \u001b[31m59.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m212.2/212.2 KB\u001b[0m \u001b[31m24.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m110.5/110.5 KB\u001b[0m \u001b[31m1.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m132.9/132.9 KB\u001b[0m \u001b[31m21.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m114.2/114.2 KB\u001b[0m \u001b[31m18.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m264.6/264.6 KB\u001b[0m \u001b[31m34.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m158.8/158.8 KB\u001b[0m \u001b[31m21.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
          ]
        }
      ],
      "source": [
        "!pip install -q datasets"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SZKAVq-6_tvv"
      },
      "source": [
        "We'll also install PyTorch Lightning and Weights and Biases, as those are the tools we'll use for training."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oiJdqe_G3Ctc",
        "outputId": "6d0ee526-623c-42a7-ff30-e8d6dcedd660"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
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            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m55.6/55.6 KB\u001b[0m \u001b[31m9.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m52.8/52.8 KB\u001b[0m \u001b[31m8.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m71.5/71.5 KB\u001b[0m \u001b[31m10.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m144.1/144.1 KB\u001b[0m \u001b[31m18.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m283.7/283.7 KB\u001b[0m \u001b[31m31.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m69.6/69.6 KB\u001b[0m \u001b[31m5.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m59.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m417.9/417.9 KB\u001b[0m \u001b[31m43.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m4.2/4.2 MB\u001b[0m \u001b[31m88.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Building wheel for pathtools (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
          ]
        }
      ],
      "source": [
        "!pip install -q lightning wandb"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cliqx5Bn-FBC"
      },
      "source": [
        "## Load dataset\n",
        "\n",
        "Let's load the dataset from the [hub](https://huggingface.co/datasets/naver-clova-ix/cord-v2)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
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          "base_uri": "https://localhost:8080/",
          "height": 425,
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          ]
        },
        "id": "WPcaDeSY-C3L",
        "outputId": "71eb75b9-973a-4e0d-8f1c-88b0a4f1a4e4"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading metadata:   0%|          | 0.00/1.05k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "372308ba59ca40f99e5ef3f67b702d90"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading readme:   0%|          | 0.00/27.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "03e23d589c734a89a53cf020ec70cb65"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading and preparing dataset imagefolder/cord-v2 (download: 2.15 GiB, generated: 2.16 GiB, post-processed: Unknown size, total: 4.31 GiB) to /root/.cache/huggingface/datasets/naver-clova-ix___parquet/naver-clova-ix--cord-v2-c97f979311033a44/0.0.0/2a3b91fbd88a2c90d1dbbb32b460cf621d31bd5b05b934492fdef7d8d6f236ec...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data files:   0%|          | 0/3 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "feccb4bd2c6e45a580aaf7cf881211ea"
            }
          },
          "metadata": {}
        },
        {
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          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "63e24130bdaa4e228f8886fbe6a37c5b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "37190369638242d8b8f18cfe0c223962"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "db47e837cc564468b7ffbc6a6fbaac15"
            }
          },
          "metadata": {}
        },
        {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7505cafcbe53438da5c6eac3c2de402a"
            }
          },
          "metadata": {}
        },
        {
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          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a45390c67e7b48d9907cfa2c5ca2c7a0"
            }
          },
          "metadata": {}
        },
        {
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          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "51628e08697841f9ac5986c1a5d66b9b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Extracting data files:   0%|          | 0/3 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "85d66e66ce5d44ee96a799b9397144ab"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating test split:   0%|          | 0/100 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating validation split:   0%|          | 0/100 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2f6116c9fa44461cad29474d156bcc76"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split:   0%|          | 0/800 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e18f203ee7ec41918aaeda02f72cf25e"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset parquet downloaded and prepared to /root/.cache/huggingface/datasets/naver-clova-ix___parquet/naver-clova-ix--cord-v2-c97f979311033a44/0.0.0/2a3b91fbd88a2c90d1dbbb32b460cf621d31bd5b05b934492fdef7d8d6f236ec. Subsequent calls will reuse this data.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/3 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "36d565a1b76248338790d76d7b0858d0"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "from datasets import load_dataset\n",
        "\n",
        "dataset = load_dataset(\"naver-clova-ix/cord-v2\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E9wkxgDlulRA"
      },
      "source": [
        "The dataset consists of 3 splits as can be seen: train, validation and test. Each example consists of an \"image\" and a \"ground_truth\" string."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Zp7RiX9B-Yeh",
        "outputId": "b5027cd7-b85f-4691-fea4-b77478617cd5"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    test: Dataset({\n",
              "        features: ['image', 'ground_truth'],\n",
              "        num_rows: 100\n",
              "    })\n",
              "    validation: Dataset({\n",
              "        features: ['image', 'ground_truth'],\n",
              "        num_rows: 100\n",
              "    })\n",
              "    train: Dataset({\n",
              "        features: ['image', 'ground_truth'],\n",
              "        num_rows: 800\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "dataset"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5SmYAHpSuoar"
      },
      "source": [
        "Let's check and visualize the first training example:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 405
        },
        "id": "1T-gdEA_-K90",
        "outputId": "3ef90fce-a585-4b01-e951-7fb32bc04028"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=259x388 at 0x7F71A40932E0>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "example = dataset['train'][0]\n",
        "image = example['image']\n",
        "# let's make the image a bit smaller when visualizing\n",
        "width, height = image.size\n",
        "display(image.resize((int(width*0.3), int(height*0.3))))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NFFju745-Y0J",
        "outputId": "b04a1e31-244d-40ac-b6b7-1b5288e8d232"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{\"gt_parse\": {\"menu\": [{\"nm\": \"Nasi Campur Bali\", \"cnt\": \"1 x\", \"price\": \"75,000\"}, {\"nm\": \"Bbk Bengil Nasi\", \"cnt\": \"1 x\", \"price\": \"125,000\"}, {\"nm\": \"MilkShake Starwb\", \"cnt\": \"1 x\", \"price\": \"37,000\"}, {\"nm\": \"Ice Lemon Tea\", \"cnt\": \"1 x\", \"price\": \"24,000\"}, {\"nm\": \"Nasi Ayam Dewata\", \"cnt\": \"1 x\", \"price\": \"70,000\"}, {\"nm\": \"Free Ice Tea\", \"cnt\": \"3 x\", \"price\": \"0\"}, {\"nm\": \"Organic Green Sa\", \"cnt\": \"1 x\", \"price\": \"65,000\"}, {\"nm\": \"Ice Tea\", \"cnt\": \"1 x\", \"price\": \"18,000\"}, {\"nm\": \"Ice Orange\", \"cnt\": \"1 x\", \"price\": \"29,000\"}, {\"nm\": \"Ayam Suir Bali\", \"cnt\": \"1 x\", \"price\": \"85,000\"}, {\"nm\": \"Tahu Goreng\", \"cnt\": \"2 x\", \"price\": \"36,000\"}, {\"nm\": \"Tempe Goreng\", \"cnt\": \"2 x\", \"price\": \"36,000\"}, {\"nm\": \"Tahu Telor Asin\", \"cnt\": \"1 x\", \"price\": \"40,000.\"}, {\"nm\": \"Nasi Goreng Samb\", \"cnt\": \"1 x\", \"price\": \"70,000\"}, {\"nm\": \"Bbk Panggang Sam\", \"cnt\": \"3 x\", \"price\": \"366,000\"}, {\"nm\": \"Ayam Sambal Hija\", \"cnt\": \"1 x\", \"price\": \"92,000\"}, {\"nm\": \"Hot Tea\", \"cnt\": \"2 x\", \"price\": \"44,000\"}, {\"nm\": \"Ice Kopi\", \"cnt\": \"1 x\", \"price\": \"32,000\"}, {\"nm\": \"Tahu Telor Asin\", \"cnt\": \"1 x\", \"price\": \"40,000\"}, {\"nm\": \"Free Ice Tea\", \"cnt\": \"1 x\", \"price\": \"0\"}, {\"nm\": \"Bebek Street\", \"cnt\": \"1 x\", \"price\": \"44,000\"}, {\"nm\": \"Ice Tea Tawar\", \"cnt\": \"1 x\", \"price\": \"18,000\"}], \"sub_total\": {\"subtotal_price\": \"1,346,000\", \"service_price\": \"100,950\", \"tax_price\": \"144,695\", \"etc\": \"-45\"}, \"total\": {\"total_price\": \"1,591,600\"}}, \"meta\": {\"version\": \"2.0.0\", \"split\": \"train\", \"image_id\": 0, \"image_size\": {\"width\": 864, \"height\": 1296}}, \"valid_line\": [{\"words\": [{\"quad\": {\"x2\": 244, \"y3\": 390, \"x3\": 244, \"y4\": 390, \"x1\": 232, \"y1\": 372, \"x4\": 232, \"y2\": 372}, \"is_key\": 0, \"row_id\": 2179893, \"text\": \"1\"}, {\"quad\": {\"x2\": 270, \"y3\": 390, \"x3\": 270, \"y4\": 390, \"x1\": 256, \"y1\": 374, \"x4\": 256, \"y2\": 374}, \"is_key\": 0, \"row_id\": 2179893, \"text\": \"x\"}], \"category\": \"menu.cnt\", \"group_id\": 3, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 354, \"y3\": 390, \"x3\": 354, \"y4\": 390, \"x1\": 302, \"y1\": 368, \"x4\": 302, \"y2\": 368}, \"is_key\": 0, \"row_id\": 2179893, \"text\": \"Nasi\"}, {\"quad\": {\"x2\": 440, \"y3\": 391, \"x3\": 439, \"y4\": 388, \"x1\": 364, \"y1\": 365, \"x4\": 363, \"y2\": 368}, \"is_key\": 0, \"row_id\": 2179893, \"text\": \"Campur\"}, {\"quad\": {\"x2\": 497, \"y3\": 385, \"x3\": 499, \"y4\": 388, \"x1\": 446, \"y1\": 365, \"x4\": 448, \"y2\": 362}, \"is_key\": 0, \"row_id\": 2179893, \"text\": \"Bali\"}], \"category\": \"menu.nm\", \"group_id\": 3, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 618, \"y3\": 388, \"x3\": 618, \"y4\": 388, 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\"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 348, \"y3\": 470, \"x3\": 348, \"y4\": 470, \"x1\": 306, \"y1\": 448, \"x4\": 306, \"y2\": 448}, \"is_key\": 0, \"row_id\": 2179896, \"text\": \"Ice\"}, {\"quad\": {\"x2\": 418, \"y3\": 468, \"x3\": 418, \"y4\": 468, \"x1\": 354, \"y1\": 446, \"x4\": 354, \"y2\": 446}, \"is_key\": 0, \"row_id\": 2179896, \"text\": \"Lemon\"}, {\"quad\": {\"x2\": 466, \"y3\": 466, \"x3\": 466, \"y4\": 466, \"x1\": 426, \"y1\": 446, \"x4\": 426, \"y2\": 446}, \"is_key\": 0, \"row_id\": 2179896, \"text\": \"Tea\"}], \"category\": \"menu.nm\", \"group_id\": 6, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 618, \"y3\": 466, \"x3\": 618, \"y4\": 466, \"x1\": 544, \"y1\": 440, \"x4\": 544, \"y2\": 440}, \"is_key\": 0, \"row_id\": 2179896, \"text\": \"24,000\"}], \"category\": \"menu.price\", \"group_id\": 6, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 250, \"y3\": 496, \"x3\": 250, \"y4\": 496, \"x1\": 238, \"y1\": 480, \"x4\": 238, \"y2\": 480}, \"is_key\": 0, \"row_id\": 2179897, \"text\": \"1\"}, {\"quad\": {\"x2\": 274, \"y3\": 496, \"x3\": 274, \"y4\": 496, \"x1\": 258, \"y1\": 480, \"x4\": 258, \"y2\": 480}, \"is_key\": 0, \"row_id\": 2179897, \"text\": \"x\"}], \"category\": \"menu.cnt\", \"group_id\": 7, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 358, \"y3\": 496, \"x3\": 358, \"y4\": 496, \"x1\": 306, \"y1\": 474, \"x4\": 306, \"y2\": 474}, \"is_key\": 0, \"row_id\": 2179897, \"text\": \"Nasi\"}, {\"quad\": {\"x2\": 420, \"y3\": 496, \"x3\": 420, \"y4\": 496, \"x1\": 366, \"y1\": 474, \"x4\": 366, \"y2\": 474}, \"is_key\": 0, \"row_id\": 2179897, \"text\": \"Ayam\"}, {\"quad\": {\"x2\": 499, \"y3\": 491, \"x3\": 500, \"y4\": 494, \"x1\": 425, \"y1\": 471, \"x4\": 426, \"y2\": 469}, \"is_key\": 0, \"row_id\": 2179897, \"text\": \"Dewata\"}], \"category\": \"menu.nm\", \"group_id\": 7, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 618, \"y3\": 492, \"x3\": 618, \"y4\": 492, \"x1\": 544, 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{\"quad\": {\"x2\": 458, \"y3\": 672, \"x3\": 458, \"y4\": 672, \"x1\": 384, \"y1\": 650, \"x4\": 384, \"y2\": 650}, \"is_key\": 0, \"row_id\": 2179904, \"text\": \"Goreng\"}], \"category\": \"menu.nm\", \"group_id\": 14, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 620, \"y3\": 668, \"x3\": 620, \"y4\": 668, \"x1\": 546, \"y1\": 644, \"x4\": 546, \"y2\": 644}, \"is_key\": 0, \"row_id\": 2179904, \"text\": \"36,000\"}], \"category\": \"menu.price\", \"group_id\": 14, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 258, \"y3\": 700, \"x3\": 258, \"y4\": 700, \"x1\": 244, \"y1\": 682, \"x4\": 244, \"y2\": 682}, \"is_key\": 0, \"row_id\": 2179905, \"text\": \"1\"}, {\"quad\": {\"x2\": 282, \"y3\": 700, \"x3\": 282, \"y4\": 700, \"x1\": 266, \"y1\": 686, \"x4\": 266, \"y2\": 686}, \"is_key\": 0, \"row_id\": 2179905, \"text\": \"x\"}], \"category\": \"menu.cnt\", \"group_id\": 15, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 364, \"y3\": 698, \"x3\": 364, \"y4\": 698, \"x1\": 312, \"y1\": 676, \"x4\": 312, \"y2\": 676}, \"is_key\": 0, \"row_id\": 2179905, \"text\": \"Tahu\"}, {\"quad\": {\"x2\": 434, \"y3\": 696, \"x3\": 434, \"y4\": 696, \"x1\": 370, \"y1\": 674, \"x4\": 370, \"y2\": 674}, \"is_key\": 0, \"row_id\": 2179905, \"text\": \"Telor\"}, {\"quad\": {\"x2\": 494, \"y3\": 694, \"x3\": 494, \"y4\": 694, \"x1\": 440, \"y1\": 672, \"x4\": 440, \"y2\": 672}, \"is_key\": 0, \"row_id\": 2179905, \"text\": \"Asin\"}], \"category\": \"menu.nm\", \"group_id\": 15, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 630, \"y3\": 690, \"x3\": 630, \"y4\": 690, \"x1\": 548, \"y1\": 668, \"x4\": 548, \"y2\": 668}, \"is_key\": 0, \"row_id\": 2179905, \"text\": \"40,000.\"}], \"category\": \"menu.price\", \"group_id\": 15, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 256, \"y3\": 726, \"x3\": 256, \"y4\": 726, \"x1\": 244, \"y1\": 708, \"x4\": 244, \"y2\": 708}, \"is_key\": 0, \"row_id\": 2179906, \"text\": \"1\"}, {\"quad\": {\"x2\": 282, \"y3\": 726, \"x3\": 282, \"y4\": 726, \"x1\": 268, \"y1\": 710, \"x4\": 268, \"y2\": 710}, \"is_key\": 0, \"row_id\": 2179906, \"text\": \"x\"}], \"category\": \"menu.cnt\", \"group_id\": 16, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 364, \"y3\": 724, \"x3\": 364, \"y4\": 724, \"x1\": 312, \"y1\": 702, \"x4\": 312, \"y2\": 702}, \"is_key\": 0, \"row_id\": 2179906, \"text\": \"Nasi\"}, {\"quad\": {\"x2\": 446, \"y3\": 722, \"x3\": 446, \"y4\": 722, \"x1\": 372, \"y1\": 700, \"x4\": 372, \"y2\": 700}, \"is_key\": 0, \"row_id\": 2179906, \"text\": \"Goreng\"}, {\"quad\": {\"x2\": 505, \"y3\": 715, \"x3\": 506, \"y4\": 718, \"x1\": 453, \"y1\": 696, \"x4\": 454, \"y2\": 693}, \"is_key\": 0, \"row_id\": 2179906, \"text\": \"Samb\"}], \"category\": \"menu.nm\", \"group_id\": 16, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 626, \"y3\": 716, \"x3\": 626, \"y4\": 716, \"x1\": 550, \"y1\": 692, \"x4\": 550, \"y2\": 692}, \"is_key\": 0, \"row_id\": 2179906, \"text\": \"70,000\"}], \"category\": \"menu.price\", \"group_id\": 16, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 256, \"y3\": 752, \"x3\": 256, \"y4\": 752, \"x1\": 242, \"y1\": 734, \"x4\": 242, \"y2\": 734}, \"is_key\": 0, \"row_id\": 2179907, \"text\": \"3\"}, {\"quad\": {\"x2\": 280, \"y3\": 752, \"x3\": 280, \"y4\": 752, \"x1\": 266, \"y1\": 738, \"x4\": 266, \"y2\": 738}, \"is_key\": 0, \"row_id\": 2179907, \"text\": \"x\"}], \"category\": \"menu.cnt\", \"group_id\": 17, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 352, \"y3\": 750, \"x3\": 352, \"y4\": 750, \"x1\": 312, \"y1\": 728, \"x4\": 312, \"y2\": 728}, \"is_key\": 0, \"row_id\": 2179907, \"text\": \"Bbk\"}, {\"quad\": {\"x2\": 457, \"y3\": 747, \"x3\": 458, \"y4\": 749, \"x1\": 357, \"y1\": 726, \"x4\": 358, \"y2\": 723}, \"is_key\": 0, \"row_id\": 2179907, \"text\": \"Panggang\"}, {\"quad\": {\"x2\": 508, \"y3\": 742, \"x3\": 508, \"y4\": 742, \"x1\": 466, \"y1\": 722, \"x4\": 466, \"y2\": 722}, \"is_key\": 0, \"row_id\": 2179907, \"text\": \"Sam\"}], \"category\": \"menu.nm\", \"group_id\": 17, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 630, \"y3\": 742, \"x3\": 630, \"y4\": 742, \"x1\": 538, \"y1\": 716, \"x4\": 538, \"y2\": 716}, \"is_key\": 0, \"row_id\": 2179907, \"text\": \"366,000\"}], \"category\": \"menu.price\", \"group_id\": 17, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 254, \"y3\": 778, \"x3\": 254, \"y4\": 778, \"x1\": 242, \"y1\": 762, \"x4\": 242, \"y2\": 762}, \"is_key\": 0, \"row_id\": 2179908, \"text\": \"1\"}, {\"quad\": {\"x2\": 280, \"y3\": 778, \"x3\": 280, \"y4\": 778, \"x1\": 266, \"y1\": 764, \"x4\": 266, \"y2\": 764}, \"is_key\": 0, \"row_id\": 2179908, \"text\": \"x\"}], \"category\": \"menu.cnt\", \"group_id\": 18, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 364, \"y3\": 778, \"x3\": 364, \"y4\": 778, \"x1\": 312, \"y1\": 754, \"x4\": 312, \"y2\": 754}, \"is_key\": 0, \"row_id\": 2179908, \"text\": \"Ayam\"}, {\"quad\": {\"x2\": 447, \"y3\": 771, \"x3\": 448, \"y4\": 774, \"x1\": 371, \"y1\": 750, \"x4\": 372, \"y2\": 747}, \"is_key\": 0, \"row_id\": 2179908, \"text\": \"Sambal\"}, {\"quad\": {\"x2\": 508, \"y3\": 772, \"x3\": 508, \"y4\": 772, \"x1\": 454, \"y1\": 746, \"x4\": 454, \"y2\": 746}, \"is_key\": 0, \"row_id\": 2179908, \"text\": \"Hija\"}], \"category\": \"menu.nm\", \"group_id\": 18, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 632, \"y3\": 768, \"x3\": 632, \"y4\": 768, \"x1\": 554, \"y1\": 742, \"x4\": 554, \"y2\": 742}, \"is_key\": 0, \"row_id\": 2179908, \"text\": \"92,000\"}], \"category\": \"menu.price\", \"group_id\": 18, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 254, \"y3\": 806, \"x3\": 254, \"y4\": 806, \"x1\": 236, \"y1\": 784, \"x4\": 236, \"y2\": 784}, \"is_key\": 0, \"row_id\": 2179909, \"text\": \"2\"}, {\"quad\": {\"x2\": 278, \"y3\": 804, \"x3\": 278, \"y4\": 804, \"x1\": 262, \"y1\": 788, \"x4\": 262, \"y2\": 788}, \"is_key\": 0, \"row_id\": 2179909, \"text\": \"x\"}], \"category\": \"menu.cnt\", \"group_id\": 19, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 352, \"y3\": 802, \"x3\": 352, \"y4\": 802, \"x1\": 310, \"y1\": 780, \"x4\": 310, \"y2\": 780}, \"is_key\": 0, \"row_id\": 2179909, \"text\": \"Hot\"}, {\"quad\": {\"x2\": 400, \"y3\": 800, \"x3\": 400, \"y4\": 800, \"x1\": 358, \"y1\": 778, \"x4\": 358, \"y2\": 778}, \"is_key\": 0, \"row_id\": 2179909, \"text\": \"Tea\"}], \"category\": \"menu.nm\", \"group_id\": 19, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 634, \"y3\": 796, \"x3\": 634, \"y4\": 796, \"x1\": 554, \"y1\": 770, \"x4\": 554, \"y2\": 770}, \"is_key\": 0, \"row_id\": 2179909, \"text\": \"44,000\"}], \"category\": \"menu.price\", \"group_id\": 19, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 252, \"y3\": 834, \"x3\": 252, \"y4\": 834, \"x1\": 240, \"y1\": 816, \"x4\": 240, \"y2\": 816}, \"is_key\": 0, \"row_id\": 2179910, \"text\": \"1\"}, {\"quad\": {\"x2\": 278, \"y3\": 832, \"x3\": 278, \"y4\": 832, \"x1\": 262, \"y1\": 816, \"x4\": 262, \"y2\": 816}, \"is_key\": 0, \"row_id\": 2179910, \"text\": \"x\"}], \"category\": \"menu.cnt\", \"group_id\": 20, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 352, \"y3\": 830, \"x3\": 352, \"y4\": 830, \"x1\": 312, \"y1\": 808, \"x4\": 312, \"y2\": 808}, \"is_key\": 0, \"row_id\": 2179910, \"text\": \"Ice\"}, {\"quad\": {\"x2\": 412, \"y3\": 830, \"x3\": 412, \"y4\": 830, \"x1\": 360, \"y1\": 804, \"x4\": 360, \"y2\": 804}, \"is_key\": 0, \"row_id\": 2179910, \"text\": \"Kopi\"}], \"category\": \"menu.nm\", \"group_id\": 20, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 636, \"y3\": 826, \"x3\": 636, \"y4\": 826, \"x1\": 556, \"y1\": 798, \"x4\": 556, \"y2\": 798}, \"is_key\": 0, \"row_id\": 2179910, \"text\": \"32,000\"}], \"category\": \"menu.price\", \"group_id\": 20, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 250, \"y3\": 862, \"x3\": 250, \"y4\": 862, \"x1\": 238, \"y1\": 844, \"x4\": 238, \"y2\": 844}, \"is_key\": 0, \"row_id\": 2179911, \"text\": \"1\"}, {\"quad\": {\"x2\": 276, \"y3\": 862, \"x3\": 276, \"y4\": 862, \"x1\": 260, \"y1\": 844, \"x4\": 260, \"y2\": 844}, \"is_key\": 0, \"row_id\": 2179911, \"text\": \"x\"}], \"category\": \"menu.cnt\", \"group_id\": 21, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 364, \"y3\": 860, \"x3\": 364, \"y4\": 860, \"x1\": 310, \"y1\": 836, \"x4\": 310, \"y2\": 836}, \"is_key\": 0, \"row_id\": 2179911, \"text\": \"Tahu\"}, {\"quad\": {\"x2\": 438, \"y3\": 858, \"x3\": 438, \"y4\": 858, \"x1\": 372, \"y1\": 834, \"x4\": 372, \"y2\": 834}, \"is_key\": 0, \"row_id\": 2179911, \"text\": \"Telor\"}, {\"quad\": {\"x2\": 500, \"y3\": 854, \"x3\": 500, \"y4\": 854, \"x1\": 444, \"y1\": 832, \"x4\": 444, \"y2\": 832}, \"is_key\": 0, \"row_id\": 2179911, \"text\": \"Asin\"}], \"category\": \"menu.nm\", \"group_id\": 21, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 638, \"y3\": 854, \"x3\": 638, \"y4\": 854, \"x1\": 558, \"y1\": 826, \"x4\": 558, \"y2\": 826}, \"is_key\": 0, \"row_id\": 2179911, \"text\": \"40,000\"}], \"category\": \"menu.price\", \"group_id\": 21, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 250, \"y3\": 892, \"x3\": 250, \"y4\": 892, \"x1\": 238, \"y1\": 872, \"x4\": 238, \"y2\": 872}, \"is_key\": 0, \"row_id\": 2179912, \"text\": \"1\"}, {\"quad\": {\"x2\": 276, \"y3\": 890, \"x3\": 276, \"y4\": 890, \"x1\": 260, \"y1\": 872, \"x4\": 260, \"y2\": 872}, \"is_key\": 0, \"row_id\": 2179912, \"text\": \"x\"}], \"category\": \"menu.cnt\", \"group_id\": 22, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 364, \"y3\": 888, \"x3\": 364, \"y4\": 888, \"x1\": 310, \"y1\": 866, \"x4\": 310, \"y2\": 866}, \"is_key\": 0, \"row_id\": 2179912, \"text\": \"Free\"}, {\"quad\": {\"x2\": 414, \"y3\": 886, \"x3\": 414, \"y4\": 886, \"x1\": 374, \"y1\": 864, \"x4\": 374, \"y2\": 864}, \"is_key\": 0, \"row_id\": 2179912, \"text\": \"Ice\"}, {\"quad\": {\"x2\": 464, \"y3\": 884, \"x3\": 464, \"y4\": 884, \"x1\": 422, \"y1\": 862, \"x4\": 422, \"y2\": 862}, \"is_key\": 0, \"row_id\": 2179912, \"text\": \"Tea\"}], \"category\": \"menu.nm\", \"group_id\": 22, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 640, \"y3\": 878, \"x3\": 640, \"y4\": 878, \"x1\": 622, \"y1\": 856, \"x4\": 622, \"y2\": 856}, \"is_key\": 0, \"row_id\": 2179912, \"text\": \"0\"}], \"category\": \"menu.price\", \"group_id\": 22, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 250, \"y3\": 920, \"x3\": 250, \"y4\": 920, \"x1\": 236, \"y1\": 900, \"x4\": 236, \"y2\": 900}, \"is_key\": 0, \"row_id\": 2179913, \"text\": \"1\"}, {\"quad\": {\"x2\": 276, \"y3\": 920, \"x3\": 276, \"y4\": 920, \"x1\": 260, \"y1\": 902, \"x4\": 260, \"y2\": 902}, \"is_key\": 0, \"row_id\": 2179913, \"text\": \"x\"}], \"category\": \"menu.cnt\", \"group_id\": 23, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 376, \"y3\": 916, \"x3\": 376, \"y4\": 916, \"x1\": 308, \"y1\": 892, \"x4\": 308, \"y2\": 892}, \"is_key\": 0, \"row_id\": 2179913, \"text\": \"Bebek\"}, {\"quad\": {\"x2\": 464, \"y3\": 914, \"x3\": 464, \"y4\": 914, \"x1\": 384, \"y1\": 890, \"x4\": 384, \"y2\": 890}, \"is_key\": 0, \"row_id\": 2179913, \"text\": \"Street\"}], \"category\": \"menu.nm\", \"group_id\": 23, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 641, \"y3\": 908, \"x3\": 642, \"y4\": 911, \"x1\": 559, \"y1\": 884, \"x4\": 560, \"y2\": 881}, \"is_key\": 0, \"row_id\": 2179913, \"text\": \"44,000\"}], \"category\": \"menu.price\", \"group_id\": 23, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 250, \"y3\": 948, \"x3\": 250, \"y4\": 948, \"x1\": 238, \"y1\": 930, \"x4\": 238, \"y2\": 930}, \"is_key\": 0, \"row_id\": 2179914, \"text\": \"1\"}, {\"quad\": {\"x2\": 276, \"y3\": 946, \"x3\": 276, \"y4\": 946, \"x1\": 260, \"y1\": 930, \"x4\": 260, \"y2\": 930}, \"is_key\": 0, \"row_id\": 2179914, \"text\": \"x\"}], \"category\": \"menu.cnt\", \"group_id\": 24, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 352, \"y3\": 946, \"x3\": 352, \"y4\": 946, \"x1\": 312, \"y1\": 924, \"x4\": 312, \"y2\": 924}, \"is_key\": 0, \"row_id\": 2179914, \"text\": \"Ice\"}, {\"quad\": {\"x2\": 402, \"y3\": 944, \"x3\": 402, \"y4\": 944, \"x1\": 360, \"y1\": 922, \"x4\": 360, \"y2\": 922}, \"is_key\": 0, \"row_id\": 2179914, \"text\": \"Tea\"}, {\"quad\": {\"x2\": 480, \"y3\": 942, \"x3\": 480, \"y4\": 942, \"x1\": 412, \"y1\": 920, \"x4\": 412, \"y2\": 920}, \"is_key\": 0, \"row_id\": 2179914, \"text\": \"Tawar\"}], \"category\": \"menu.nm\", \"group_id\": 24, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 642, \"y3\": 938, \"x3\": 642, \"y4\": 938, \"x1\": 564, \"y1\": 912, \"x4\": 564, \"y2\": 912}, \"is_key\": 0, \"row_id\": 2179914, \"text\": \"18,000\"}], \"category\": \"menu.price\", \"group_id\": 24, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 479, \"y3\": 998, \"x3\": 481, \"y4\": 1005, \"x1\": 360, \"y1\": 979, \"x4\": 362, \"y2\": 973}, \"is_key\": 1, \"row_id\": 2179915, \"text\": \"Sub-Total\"}, {\"quad\": {\"x2\": 645, \"y3\": 995, \"x3\": 646, \"y4\": 998, \"x1\": 527, \"y1\": 970, \"x4\": 528, \"y2\": 967}, \"is_key\": 0, \"row_id\": 2179915, \"text\": \"1,346,000\"}], \"category\": \"sub_total.subtotal_price\", \"group_id\": 25, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 481, \"y3\": 1027, \"x3\": 482, \"y4\": 1030, \"x1\": 387, \"y1\": 1007, \"x4\": 388, \"y2\": 1004}, \"is_key\": 1, \"row_id\": 2179916, \"text\": \"Service\"}, {\"quad\": {\"x2\": 646, \"y3\": 1026, \"x3\": 646, \"y4\": 1026, \"x1\": 554, \"y1\": 998, \"x4\": 554, \"y2\": 998}, \"is_key\": 0, \"row_id\": 2179916, \"text\": \"100,950\"}], \"category\": \"sub_total.service_price\", \"group_id\": 25, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 482, \"y3\": 1056, \"x3\": 482, \"y4\": 1056, \"x1\": 438, \"y1\": 1032, \"x4\": 438, \"y2\": 1032}, \"is_key\": 1, \"row_id\": 2179917, \"text\": \"PB1\"}, {\"quad\": {\"x2\": 648, \"y3\": 1052, \"x3\": 648, \"y4\": 1052, \"x1\": 556, \"y1\": 1026, \"x4\": 556, \"y2\": 1026}, \"is_key\": 0, \"row_id\": 2179917, \"text\": \"144,695\"}], \"category\": \"sub_total.tax_price\", \"group_id\": 25, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 481, \"y3\": 1085, \"x3\": 482, \"y4\": 1088, \"x1\": 375, \"y1\": 1063, \"x4\": 376, \"y2\": 1061}, \"is_key\": 1, \"row_id\": 2179918, \"text\": \"Rounding\"}, {\"quad\": {\"x2\": 648, \"y3\": 1078, \"x3\": 648, \"y4\": 1078, \"x1\": 606, \"y1\": 1054, \"x4\": 606, \"y2\": 1054}, \"is_key\": 0, \"row_id\": 2179918, \"text\": \"-45\"}], \"category\": \"sub_total.etc\", \"group_id\": 25, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 334, \"y3\": 1162, \"x3\": 334, \"y4\": 1162, \"x1\": 266, \"y1\": 1142, \"x4\": 266, \"y2\": 1142}, \"is_key\": 1, \"row_id\": 2179919, \"text\": \"Grand\"}, {\"quad\": {\"x2\": 408, \"y3\": 1160, \"x3\": 408, \"y4\": 1160, \"x1\": 340, \"y1\": 1138, \"x4\": 340, \"y2\": 1138}, \"is_key\": 1, \"row_id\": 2179919, \"text\": \"Total\"}, {\"quad\": {\"x2\": 647, \"y3\": 1153, \"x3\": 649, \"y4\": 1161, \"x1\": 418, \"y1\": 1117, \"x4\": 420, \"y2\": 1108}, \"is_key\": 0, \"row_id\": 2179919, \"text\": \"1,591,600\"}], \"category\": \"total.total_price\", \"group_id\": 26, \"sub_group_id\": 0}], \"roi\": {}, \"repeating_symbol\": [], \"dontcare\": []}\n"
          ]
        }
      ],
      "source": [
        "# let's load the corresponding JSON dictionary (as string representation)\n",
        "ground_truth = example['ground_truth']\n",
        "print(ground_truth)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sVvw9bocuxQ7"
      },
      "source": [
        "Using Python's `literal_eval` function, you can turn it into an actual Python dictionary:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qsRNIuLk-ccZ",
        "outputId": "26e84d57-b8e6-4dc0-f9a1-fb023c7f870d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'menu': [{'nm': 'Nasi Campur Bali', 'cnt': '1 x', 'price': '75,000'},\n",
              "  {'nm': 'Bbk Bengil Nasi', 'cnt': '1 x', 'price': '125,000'},\n",
              "  {'nm': 'MilkShake Starwb', 'cnt': '1 x', 'price': '37,000'},\n",
              "  {'nm': 'Ice Lemon Tea', 'cnt': '1 x', 'price': '24,000'},\n",
              "  {'nm': 'Nasi Ayam Dewata', 'cnt': '1 x', 'price': '70,000'},\n",
              "  {'nm': 'Free Ice Tea', 'cnt': '3 x', 'price': '0'},\n",
              "  {'nm': 'Organic Green Sa', 'cnt': '1 x', 'price': '65,000'},\n",
              "  {'nm': 'Ice Tea', 'cnt': '1 x', 'price': '18,000'},\n",
              "  {'nm': 'Ice Orange', 'cnt': '1 x', 'price': '29,000'},\n",
              "  {'nm': 'Ayam Suir Bali', 'cnt': '1 x', 'price': '85,000'},\n",
              "  {'nm': 'Tahu Goreng', 'cnt': '2 x', 'price': '36,000'},\n",
              "  {'nm': 'Tempe Goreng', 'cnt': '2 x', 'price': '36,000'},\n",
              "  {'nm': 'Tahu Telor Asin', 'cnt': '1 x', 'price': '40,000.'},\n",
              "  {'nm': 'Nasi Goreng Samb', 'cnt': '1 x', 'price': '70,000'},\n",
              "  {'nm': 'Bbk Panggang Sam', 'cnt': '3 x', 'price': '366,000'},\n",
              "  {'nm': 'Ayam Sambal Hija', 'cnt': '1 x', 'price': '92,000'},\n",
              "  {'nm': 'Hot Tea', 'cnt': '2 x', 'price': '44,000'},\n",
              "  {'nm': 'Ice Kopi', 'cnt': '1 x', 'price': '32,000'},\n",
              "  {'nm': 'Tahu Telor Asin', 'cnt': '1 x', 'price': '40,000'},\n",
              "  {'nm': 'Free Ice Tea', 'cnt': '1 x', 'price': '0'},\n",
              "  {'nm': 'Bebek Street', 'cnt': '1 x', 'price': '44,000'},\n",
              "  {'nm': 'Ice Tea Tawar', 'cnt': '1 x', 'price': '18,000'}],\n",
              " 'sub_total': {'subtotal_price': '1,346,000',\n",
              "  'service_price': '100,950',\n",
              "  'tax_price': '144,695',\n",
              "  'etc': '-45'},\n",
              " 'total': {'total_price': '1,591,600'}}"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "from ast import literal_eval\n",
        "\n",
        "literal_eval(ground_truth)['gt_parse']"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "23_XHpOJ-kKp"
      },
      "source": [
        "## Load model and processor\n",
        "\n",
        "Next, let's load the model and its processor from the [hub](https://huggingface.co/models?other=pix2struct). Here we just use the pre-trained only, base-sized model, but note that there are 20 different checkpoints released on the hub."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "KdsGlgt8-lU3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241,
          "referenced_widgets": [
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        },
        "outputId": "4e7b1871-e933-4643-9bc0-f329a9eeab90"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
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              "version_major": 2,
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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              "Downloading spiece.model:   0%|          | 0.00/851k [00:00<?, ?B/s]"
            ],
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              "version_major": 2,
              "version_minor": 0,
              "model_id": "bf26b383f92944db8340c1f594cf821a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)/main/tokenizer.json:   0%|          | 0.00/3.27M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "438bcfdc975e417ea9227ef4ea10c418"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)cial_tokens_map.json:   0%|          | 0.00/2.20k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5a13eceb7d8c40beb901fb5ab9124dfc"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/4.92k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3f503c5e597b469388792081bb34e8de"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading pytorch_model.bin:   0%|          | 0.00/1.13G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "495ca165e46448138a9ae3c76f44f84e"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "from transformers import Pix2StructForConditionalGeneration, AutoProcessor\n",
        "\n",
        "repo_id = \"google/pix2struct-base\"\n",
        "\n",
        "processor = AutoProcessor.from_pretrained(repo_id)\n",
        "model = Pix2StructForConditionalGeneration.from_pretrained(repo_id, is_encoder_decoder=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kx75D66l_XWN"
      },
      "source": [
        "## Create PyTorch dataset\n",
        "\n",
        "We create a regular PyTorch Dataset class which returns examples of the data, prepared for the model."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "Z80qW5QD_R19"
      },
      "outputs": [],
      "source": [
        "import json\n",
        "import random\n",
        "from typing import Any, List\n",
        "from torch.utils.data import Dataset\n",
        "\n",
        "added_tokens = []\n",
        "\n",
        "class ImageCaptioningDataset(Dataset):\n",
        "    def __init__(\n",
        "        self,\n",
        "        dataset_name_or_path: str,\n",
        "        max_patches: int = 1024,\n",
        "        max_length: int = 512,\n",
        "        split: str = \"train\",\n",
        "        ignore_id: int = -100,\n",
        "        task_start_token: str = \"\",\n",
        "        prompt_end_token: str = None,\n",
        "        sort_json_key: bool = True,\n",
        "    ):\n",
        "        super().__init__()\n",
        "\n",
        "        self.split = split\n",
        "        self.dataset = load_dataset(dataset_name_or_path, split=self.split)\n",
        "        self.max_patches = max_patches\n",
        "        self.max_length = max_length\n",
        "        self.ignore_id = ignore_id\n",
        "        self.task_start_token = task_start_token\n",
        "        self.prompt_end_token = prompt_end_token if prompt_end_token else task_start_token\n",
        "        self.sort_json_key = sort_json_key\n",
        "\n",
        "        self.gt_token_sequences = []\n",
        "        for ground_truth in self.dataset[\"ground_truth\"]:\n",
        "            ground_truth = json.loads(ground_truth)\n",
        "            if \"gt_parses\" in ground_truth:  # when multiple ground truths are available, e.g., docvqa\n",
        "                assert isinstance(ground_truth[\"gt_parses\"], list)\n",
        "                gt_jsons = ground_truth[\"gt_parses\"]\n",
        "            else:\n",
        "                assert \"gt_parse\" in ground_truth and isinstance(ground_truth[\"gt_parse\"], dict)\n",
        "                gt_jsons = [ground_truth[\"gt_parse\"]]\n",
        "\n",
        "            self.gt_token_sequences.append(\n",
        "                [\n",
        "                    self.json2token(\n",
        "                        gt_json,\n",
        "                        update_special_tokens_for_json_key=self.split == \"train\",\n",
        "                        sort_json_key=self.sort_json_key,\n",
        "                    )\n",
        "                    for gt_json in gt_jsons  # load json from list of json\n",
        "                ]\n",
        "            )\n",
        "\n",
        "        self.add_tokens([self.task_start_token, self.prompt_end_token])\n",
        "        self.prompt_end_token_id = processor.tokenizer.convert_tokens_to_ids(self.prompt_end_token)\n",
        "\n",
        "    def json2token(self, obj: Any, update_special_tokens_for_json_key: bool = True, sort_json_key: bool = True):\n",
        "        \"\"\"\n",
        "        Convert an ordered JSON object into a token sequence\n",
        "        \"\"\"\n",
        "        if type(obj) == dict:\n",
        "            if len(obj) == 1 and \"text_sequence\" in obj:\n",
        "                return obj[\"text_sequence\"]\n",
        "            else:\n",
        "                output = \"\"\n",
        "                if sort_json_key:\n",
        "                    keys = sorted(obj.keys(), reverse=True)\n",
        "                else:\n",
        "                    keys = obj.keys()\n",
        "                for k in keys:\n",
        "                    if update_special_tokens_for_json_key:\n",
        "                        self.add_tokens([fr\"<s_{k}>\", fr\"</s_{k}>\"])\n",
        "                    output += (\n",
        "                        fr\"<s_{k}>\"\n",
        "                        + self.json2token(obj[k], update_special_tokens_for_json_key, sort_json_key)\n",
        "                        + fr\"</s_{k}>\"\n",
        "                    )\n",
        "                return output\n",
        "        elif type(obj) == list:\n",
        "            return r\"<sep/>\".join(\n",
        "                [self.json2token(item, update_special_tokens_for_json_key, sort_json_key) for item in obj]\n",
        "            )\n",
        "        else:\n",
        "            obj = str(obj)\n",
        "            if f\"<{obj}/>\" in added_tokens:\n",
        "                obj = f\"<{obj}/>\"  # for categorical special tokens\n",
        "            return obj\n",
        "    \n",
        "    def add_tokens(self, list_of_tokens: List[str]):\n",
        "        \"\"\"\n",
        "        Add special tokens to tokenizer and resize the token embeddings of the decoder\n",
        "        \"\"\"\n",
        "        newly_added_num = processor.tokenizer.add_tokens(list_of_tokens)\n",
        "        if newly_added_num > 0:\n",
        "            model.decoder.resize_token_embeddings(len(processor.tokenizer))\n",
        "            added_tokens.extend(list_of_tokens)\n",
        "    \n",
        "    def __len__(self) -> int:\n",
        "        return len(self.dataset)\n",
        "\n",
        "    def __getitem__(self, idx):\n",
        "        item = self.dataset[idx]\n",
        "\n",
        "        # prepare inputs\n",
        "        encoding = processor(images=item[\"image\"], max_patches=self.max_patches, return_tensors=\"pt\")\n",
        "        encoding = {k:v.squeeze() for k,v in encoding.items()}\n",
        "        \n",
        "        # prepare targets\n",
        "        target_sequence = random.choice(self.gt_token_sequences[idx])  # can be more than one, e.g., DocVQA Task 1\n",
        "        input_ids = processor.tokenizer(\n",
        "            target_sequence,\n",
        "            max_length=self.max_length,\n",
        "            padding=\"max_length\",\n",
        "            truncation=True,\n",
        "            return_tensors=\"pt\",\n",
        "        ).input_ids\n",
        "\n",
        "        labels = input_ids.squeeze().clone()\n",
        "        labels[labels == processor.tokenizer.pad_token_id] = self.ignore_id  # model doesn't need to predict pad token\n",
        "        encoding[\"labels\"] = labels\n",
        "        # labels[: torch.nonzero(labels == self.prompt_end_token_id).sum() + 1] = self.ignore_id  # model doesn't need to predict prompt (for VQA)\n",
        "        return encoding, target_sequence"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R9wfda9WKzM5",
        "outputId": "e6b854ec-b0c3-44bd-da2a-167aba138c44"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:datasets.builder:Found cached dataset parquet (/root/.cache/huggingface/datasets/naver-clova-ix___parquet/naver-clova-ix--cord-v2-c97f979311033a44/0.0.0/2a3b91fbd88a2c90d1dbbb32b460cf621d31bd5b05b934492fdef7d8d6f236ec)\n",
            "WARNING:datasets.builder:Found cached dataset parquet (/root/.cache/huggingface/datasets/naver-clova-ix___parquet/naver-clova-ix--cord-v2-c97f979311033a44/0.0.0/2a3b91fbd88a2c90d1dbbb32b460cf621d31bd5b05b934492fdef7d8d6f236ec)\n"
          ]
        }
      ],
      "source": [
        "train_dataset = ImageCaptioningDataset(\"naver-clova-ix/cord-v2\",\n",
        "                                       split=\"train\", sort_json_key=False) # cord dataset is preprocessed, so no need for this\n",
        "val_dataset = ImageCaptioningDataset(\"naver-clova-ix/cord-v2\",\n",
        "                                       split=\"validation\", sort_json_key=False) # cord dataset is preprocessed, so no need for this"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "W0tU945QLYpS",
        "outputId": "1bebe905-e2cf-49c3-9b03-01128a74c85d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "dict_keys(['flattened_patches', 'attention_mask', 'labels'])\n"
          ]
        }
      ],
      "source": [
        "encoding, target_sequence = train_dataset[0]\n",
        "print(encoding.keys())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K4xWnPqmLaJO",
        "outputId": "f7697c2f-14f9-4422-cf04-ed1f2e3519c4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<s_menu><s_nm> Nasi Campur Bali</s_nm><s_cnt> 1 x</s_cnt><s_price> 75,000</s_price> <sep/><s_nm> Bbk Bengil Nasi</s_nm><s_cnt> 1 x</s_cnt><s_price> 125,000</s_price> <sep/><s_nm> MilkShake Starwb</s_nm><s_cnt> 1 x</s_cnt><s_price> 37,000</s_price> <sep/><s_nm> Ice Lemon Tea</s_nm><s_cnt> 1 x</s_cnt><s_price> 24,000</s_price> <sep/><s_nm> Nasi Ayam Dewata</s_nm><s_cnt> 1 x</s_cnt><s_price> 70,000</s_price> <sep/><s_nm> Free Ice Tea</s_nm><s_cnt> 3 x</s_cnt><s_price> 0</s_price> <sep/><s_nm> Organic Green Sa</s_nm><s_cnt> 1 x</s_cnt><s_price> 65,000</s_price> <sep/><s_nm> Ice Tea</s_nm><s_cnt> 1 x</s_cnt><s_price> 18,000</s_price> <sep/><s_nm> Ice Orange</s_nm><s_cnt> 1 x</s_cnt><s_price> 29,000</s_price> <sep/><s_nm> Ayam Suir Bali</s_nm><s_cnt> 1 x</s_cnt><s_price> 85,000</s_price> <sep/><s_nm> Tahu Goreng</s_nm><s_cnt> 2 x</s_cnt><s_price> 36,000</s_price> <sep/><s_nm> Tempe Goreng</s_nm><s_cnt> 2 x</s_cnt><s_price> 36,000</s_price> <sep/><s_nm> Tahu Telor Asin</s_nm><s_cnt> 1 x</s_cnt><s_price> 40,000.</s_price> <sep/><s_nm> Nasi Goreng Samb</s_nm><s_cnt> 1 x</s_cnt><s_price> 70,000</s_price> <sep/><s_nm> Bbk Panggang Sam</s_nm><s_cnt> 3 x</s_cnt><s_price> 366,000</s_price> <sep/><s_nm> Ayam Sambal Hija</s_nm><s_cnt> 1 x</s_cnt><s_price> 92,000</s_price> <sep/><s_nm> Hot Tea</s_nm><s_cnt> 2 x</s_cnt><s_price> 44,000</s_price> <sep/><s_nm> Ice Kopi</s_nm><s_cnt> 1 x</s_cnt><s_price> 32,000</s_price> <sep/><s_nm> Tahu Telor Asin</s_nm><s_cnt> 1 x</s_cnt><s_price> 40,000</s_price> <sep/><s_nm> Free Ice Tea</s_nm><s_cnt> 1 x</s_cnt><s_price> 0</s_price> <sep/><s_nm> Bebek Street</s_nm><s_cnt> 1 x</s_cnt><s_price> 44,000</s_price> <sep/><s_nm> Ice Tea Tawar</s_nm><s_cnt> 1 x</s_cnt><s_price> 18,000</s_price></s_menu><s_sub_total><s_subtotal_price> 1,346,</s>\n"
          ]
        }
      ],
      "source": [
        "print(processor.decode([id.item() for id in encoding[\"labels\"] if id != -100]))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4NsqZcOqRznx",
        "outputId": "06e9f89a-80ab-4932-d3ea-72a76d172759"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<s_menu><s_nm>Nasi Campur Bali</s_nm><s_cnt>1 x</s_cnt><s_price>75,000</s_price><sep/><s_nm>Bbk Bengil Nasi</s_nm><s_cnt>1 x</s_cnt><s_price>125,000</s_price><sep/><s_nm>MilkShake Starwb</s_nm><s_cnt>1 x</s_cnt><s_price>37,000</s_price><sep/><s_nm>Ice Lemon Tea</s_nm><s_cnt>1 x</s_cnt><s_price>24,000</s_price><sep/><s_nm>Nasi Ayam Dewata</s_nm><s_cnt>1 x</s_cnt><s_price>70,000</s_price><sep/><s_nm>Free Ice Tea</s_nm><s_cnt>3 x</s_cnt><s_price>0</s_price><sep/><s_nm>Organic Green Sa</s_nm><s_cnt>1 x</s_cnt><s_price>65,000</s_price><sep/><s_nm>Ice Tea</s_nm><s_cnt>1 x</s_cnt><s_price>18,000</s_price><sep/><s_nm>Ice Orange</s_nm><s_cnt>1 x</s_cnt><s_price>29,000</s_price><sep/><s_nm>Ayam Suir Bali</s_nm><s_cnt>1 x</s_cnt><s_price>85,000</s_price><sep/><s_nm>Tahu Goreng</s_nm><s_cnt>2 x</s_cnt><s_price>36,000</s_price><sep/><s_nm>Tempe Goreng</s_nm><s_cnt>2 x</s_cnt><s_price>36,000</s_price><sep/><s_nm>Tahu Telor Asin</s_nm><s_cnt>1 x</s_cnt><s_price>40,000.</s_price><sep/><s_nm>Nasi Goreng Samb</s_nm><s_cnt>1 x</s_cnt><s_price>70,000</s_price><sep/><s_nm>Bbk Panggang Sam</s_nm><s_cnt>3 x</s_cnt><s_price>366,000</s_price><sep/><s_nm>Ayam Sambal Hija</s_nm><s_cnt>1 x</s_cnt><s_price>92,000</s_price><sep/><s_nm>Hot Tea</s_nm><s_cnt>2 x</s_cnt><s_price>44,000</s_price><sep/><s_nm>Ice Kopi</s_nm><s_cnt>1 x</s_cnt><s_price>32,000</s_price><sep/><s_nm>Tahu Telor Asin</s_nm><s_cnt>1 x</s_cnt><s_price>40,000</s_price><sep/><s_nm>Free Ice Tea</s_nm><s_cnt>1 x</s_cnt><s_price>0</s_price><sep/><s_nm>Bebek Street</s_nm><s_cnt>1 x</s_cnt><s_price>44,000</s_price><sep/><s_nm>Ice Tea Tawar</s_nm><s_cnt>1 x</s_cnt><s_price>18,000</s_price></s_menu><s_sub_total><s_subtotal_price>1,346,000</s_subtotal_price><s_service_price>100,950</s_service_price><s_tax_price>144,695</s_tax_price><s_etc>-45</s_etc></s_sub_total><s_total><s_total_price>1,591,600</s_total_price></s_total>\n"
          ]
        }
      ],
      "source": [
        "print(target_sequence)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nd7FSJsXhNeQ",
        "outputId": "cc0ee98d-9288-4356-e4ab-b410d629fe9a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of added tokens: 54\n",
            "['<s_menu>', '</s_menu>', '<s_nm>', '</s_nm>', '<s_cnt>', '</s_cnt>', '<s_price>', '</s_price>', '<s_sub_total>', '</s_sub_total>', '<s_subtotal_price>', '</s_subtotal_price>', '<s_service_price>', '</s_service_price>', '<s_tax_price>', '</s_tax_price>', '<s_etc>', '</s_etc>', '<s_total>', '</s_total>', '<s_total_price>', '</s_total_price>', '<s_sub>', '</s_sub>', '<s_cashprice>', '</s_cashprice>', '<s_changeprice>', '</s_changeprice>', '<s_menutype_cnt>', '</s_menutype_cnt>', '<s_menuqty_cnt>', '</s_menuqty_cnt>', '<s_discount_price>', '</s_discount_price>', '<s_unitprice>', '</s_unitprice>', '<s_total_etc>', '</s_total_etc>', '<s_creditcardprice>', '</s_creditcardprice>', '<s_num>', '</s_num>', '<s_discountprice>', '</s_discountprice>', '<s_emoneyprice>', '</s_emoneyprice>', '<s_void_menu>', '</s_void_menu>', '<s_othersvc_price>', '</s_othersvc_price>', '<s_vatyn>', '</s_vatyn>', '<s_itemsubtotal>', '</s_itemsubtotal>']\n"
          ]
        }
      ],
      "source": [
        "print(\"Number of added tokens:\", len(added_tokens))\n",
        "print(added_tokens)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gaFIRnptjx0D",
        "outputId": "82fe3db4-10b2-402b-c899-fc5f22bc8f9a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "50398"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ],
      "source": [
        "len(processor.tokenizer)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rlu31GWsN25m"
      },
      "source": [
        "## Create PyTorch DataLoaders\n",
        "\n",
        "Next, we create PyTorch DataLoader which allow us to get batches of the data."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "wGOTH8lGMxs1"
      },
      "outputs": [],
      "source": [
        "from torch.utils.data import DataLoader\n",
        "import multiprocessing\n",
        "\n",
        "num_cores = multiprocessing.cpu_count()\n",
        "\n",
        "train_dataloader = DataLoader(train_dataset, batch_size=1, shuffle=True, num_workers=num_cores)\n",
        "val_dataloader = DataLoader(val_dataset, batch_size=1, num_workers=num_cores)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "w2fuLf2yOOLQ"
      },
      "outputs": [],
      "source": [
        "# get first batch\n",
        "batch = next(iter(train_dataloader))\n",
        "encoding, target_sequences = batch"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_-3hKE1zPF0g",
        "outputId": "ea16cacb-109b-4305-8264-5d7012a9632f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "flattened_patches torch.Size([1, 1024, 770])\n",
            "attention_mask torch.Size([1, 1024])\n",
            "labels torch.Size([1, 512])\n"
          ]
        }
      ],
      "source": [
        "for k,v in encoding.items():\n",
        "  print(k,v.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WNzgXbBRDVEn",
        "outputId": "aafd6d0a-9627-44b4-d746-5a512b3dc783"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['<s_menu>', '<s_nm>', 'BLACK', 'PR', 'A', 'WN', 'PAS', 'TA', '</s_nm>', '<s_cnt>', '', '1', '</s_cnt>', '<s_price>', '', '8', '0', ',', '5', '0', '0', '</s_price>', '<', 'sep', '/>', '<s_nm>', 'CAR', 'BO', 'NA', 'RA', '</s_nm>', '<s_cnt>', '', '1', '</s_cnt>', '<s_price>', '', '7', '0', ',', '5', '0', '0', '</s_price>', '<', 'sep', '/>', '<s_nm>', 'EA', 'RL', 'GR', 'EY', 'MIL', 'K', 'TE', 'A', '</s_nm>', '<s_cnt>', '', '1', '</s_cnt>', '<s_price>', '', '5', '7', ',', '0', '0', '0', '</s_price>', '</s_menu>', '<s_sub_total>', '<s_subtotal_price>', '', '2', '0', '8', ',', '0', '0', '0', '</s_subtotal_price>', '<s_service_price>', '', '1', '2', ',', '4', '8', '0', '</s_service_price>', '<s_tax_price>', '', '2', '2', ',', '0', '4', '8', '</s_tax_price>', '</s_sub_total>', '<s_total>', '<s_total_price>', '', '2', '4', '2', ',', '5', '2', '8', '</s_total_price>', '</s_total>', '</s>']\n"
          ]
        }
      ],
      "source": [
        "print(processor.batch_decode([id for id in encoding[\"labels\"].squeeze().tolist() if id != -100]))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5nRNyToFSyK2"
      },
      "source": [
        "## Define Lighting module\n",
        "\n",
        "As we'll train the model using [PyTorch Lightning](https://lightning.ai/docs/pytorch/stable/), we define a so-called LightningModule, which is an `nn.Module` with additional functionality such that we don't need to take care of device placement etc."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "id": "kKcJw8xRSZnx"
      },
      "outputs": [],
      "source": [
        "from pathlib import Path\n",
        "import re\n",
        "from nltk import edit_distance\n",
        "import numpy as np\n",
        "import wandb\n",
        "\n",
        "import torch\n",
        "\n",
        "from transformers.optimization import Adafactor, get_cosine_schedule_with_warmup\n",
        "\n",
        "import pytorch_lightning as pl\n",
        "\n",
        "\n",
        "class Pix2Struct(pl.LightningModule):\n",
        "    def __init__(self, config, processor, model):\n",
        "        super().__init__()\n",
        "        self.config = config\n",
        "        self.processor = processor\n",
        "        self.model = model\n",
        "\n",
        "    def training_step(self, batch, batch_idx):\n",
        "        encoding, _ = batch\n",
        "        \n",
        "        outputs = self.model(**encoding)\n",
        "        loss = outputs.loss\n",
        "        self.log(\"train_loss\", loss)\n",
        "        return loss\n",
        "\n",
        "    def validation_step(self, batch, batch_idx, dataset_idx=0):\n",
        "        encoding, answers = batch\n",
        "        flattened_patches, attention_mask = encoding[\"flattened_patches\"], encoding[\"attention_mask\"]\n",
        "        batch_size = flattened_patches.shape[0]\n",
        "        # we feed the prompt to the model\n",
        "        decoder_input_ids = torch.full((batch_size, 1), self.model.config.text_config.decoder_start_token_id, device=self.device)\n",
        "        \n",
        "        outputs = self.model.generate(flattened_patches=flattened_patches,\n",
        "                                      attention_mask=attention_mask,\n",
        "                                      # decoder_input_ids=decoder_input_ids,\n",
        "                                      max_new_tokens=512,\n",
        "                                      return_dict_in_generate=True,)\n",
        "    \n",
        "        predictions = []\n",
        "        for seq in self.processor.tokenizer.batch_decode(outputs.sequences):\n",
        "            seq = seq.replace(self.processor.tokenizer.eos_token, \"\").replace(self.processor.tokenizer.pad_token, \"\")\n",
        "            # seq = re.sub(r\"<.*?>\", \"\", seq, count=1).strip()  # remove first task start token\n",
        "            predictions.append(seq)\n",
        "\n",
        "        scores = []\n",
        "        for pred, answer in zip(predictions, answers):\n",
        "            # pred = re.sub(r\"(?:(?<=>) | (?=\", \"\", answer, count=1)\n",
        "            answer = answer.replace(self.processor.tokenizer.eos_token, \"\")\n",
        "            scores.append(edit_distance(pred, answer) / max(len(pred), len(answer)))\n",
        "            \n",
        "            if self.config.get(\"verbose\", False) and len(scores) == 1:\n",
        "                print(f\"Prediction: {pred}\")\n",
        "                print(f\"    Answer: {answer}\")\n",
        "                print(f\" Normed ED: {scores[0]}\")\n",
        "\n",
        "        self.log(\"val_edit_distance\", np.mean(scores)) \n",
        "        \n",
        "        return scores\n",
        "\n",
        "    def configure_optimizers(self):\n",
        "        optimizer = Adafactor(self.parameters(), scale_parameter=False, relative_step=False, lr=self.config.get(\"lr\"), weight_decay=1e-05)\n",
        "        scheduler = get_cosine_schedule_with_warmup(optimizer,\n",
        "                                                    num_warmup_steps=self.config.get(\"num_warmup_steps\"),\n",
        "                                                    num_training_steps=self.config.get(\"max_steps\"))\n",
        "        \n",
        "        return [optimizer], [scheduler]\n",
        "\n",
        "    def train_dataloader(self):\n",
        "        return train_dataloader\n",
        "\n",
        "    def val_dataloader(self):\n",
        "        return val_dataloader"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "njOGca6cFTPq"
      },
      "source": [
        "## Train!\n",
        "\n",
        "Next, we instantiate the model and define a PyTorch Lightning Trainer. We can specify all kinds of things, such as on which devices we'd like to train, whether to use gradient clipping, logging to Weights and Biases etc. Check the [docs](https://lightning.ai/docs/pytorch/stable/api/lightning.pytorch.trainer.trainer.Trainer.html#lightning.pytorch.trainer.trainer.Trainer) for all arguments you can pass to the Trainer - there are a lot of them!\n",
        "\n",
        "Note: the model seems to converge **much slower** than Donut on this dataset. This may be due to a domain shift: Pix2Struct has been pre-trained on web page images (by predicting HTML on masked image portions). I haven't played too much with hyperparameters, I'm using the Adam optimizer with learning rate 1e-5. For reference, the authors use the AdaFactor optimizer with weight decay rate of 1e-5, with a learning rate that starts with 0.001 and goes up linearly to 0.01 for 1K steps and then a cosine decay for the rest of the training.\n",
        "\n",
        "Update: upgraded the notebook to use Adafactor with cosine decay learning rate schedule. Seeing faster (but still pretty slow) convergence.\n",
        "\n",
        "If you find hyperparameters with great convergence, please share them by opening an issue on my [Transformers-Tutorials](https://github.com/NielsRogge/Transformers-Tutorials) repository."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "id": "PtCXARL-Fijm"
      },
      "outputs": [],
      "source": [
        "config = {\n",
        "          \"num_warmup_steps\": 1000,\n",
        "          \"max_steps\": 30000,\n",
        "          \"lr\": 0.01,\n",
        "          \"check_val_every_n_epoch\": 5,\n",
        "          \"gradient_clip_val\": 1.0,\n",
        "          \"warmup_steps\": 300, # 800/8*30/10, 10%\n",
        "          \"accumulate_grad_batches\": 8,\n",
        "          \"verbose\": True,\n",
        "          }\n",
        "\n",
        "pl_module = Pix2Struct(config, processor, model)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 310,
          "referenced_widgets": [
            "2244b2446b584ebab51fed930218b53d",
            "095c21dc2d6e49a99feefa0afc619549",
            "f6efdf3f4a5f4734a33dd8b6afe9d141",
            "610aad51e1cb4d8c83eef3f640045708",
            "699d62e1adb04fd2aac0f76d408f516e",
            "abc20089dd704dc4a4c7889f3850e83e",
            "6642145a14c940a7ba94a3068f82260a",
            "3dc81f76c4ec4f44806888668f92fea3"
          ]
        },
        "id": "MiXIlN1ATB9l",
        "outputId": "46d47784-6747-421b-b557-2ac122281b9b"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "Waiting for W&B process to finish... <strong style=\"color:green\">(success).</strong>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "VBox(children=(Label(value='0.001 MB of 0.003 MB uploaded (0.000 MB deduped)\\r'), FloatProgress(value=0.457632…"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2244b2446b584ebab51fed930218b53d"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              " View run <strong style=\"color:#cdcd00\">demo-run-pix2struct-adafactor-v2</strong> at: <a href='https://wandb.ai/nielsrogge/Pix2Struct/runs/4oyjjdpa' target=\"_blank\">https://wandb.ai/nielsrogge/Pix2Struct/runs/4oyjjdpa</a><br/>Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "Find logs at: <code>./wandb/run-20230402_192550-4oyjjdpa/logs</code>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "Tracking run with wandb version 0.14.0"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "Run data is saved locally in <code>./wandb/run-20230402_192934-thkzcdl3</code>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "Syncing run <strong><a href='https://wandb.ai/nielsrogge/Pix2Struct/runs/thkzcdl3' target=\"_blank\">demo-run-pix2struct-adafactor-v2</a></strong> to <a href='https://wandb.ai/nielsrogge/Pix2Struct' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/run' target=\"_blank\">docs</a>)<br/>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              " View project at <a href='https://wandb.ai/nielsrogge/Pix2Struct' target=\"_blank\">https://wandb.ai/nielsrogge/Pix2Struct</a>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              " View run at <a href='https://wandb.ai/nielsrogge/Pix2Struct/runs/thkzcdl3' target=\"_blank\">https://wandb.ai/nielsrogge/Pix2Struct/runs/thkzcdl3</a>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:pytorch_lightning.utilities.rank_zero:GPU available: True (cuda), used: True\n",
            "INFO:pytorch_lightning.utilities.rank_zero:TPU available: False, using: 0 TPU cores\n",
            "INFO:pytorch_lightning.utilities.rank_zero:IPU available: False, using: 0 IPUs\n",
            "INFO:pytorch_lightning.utilities.rank_zero:HPU available: False, using: 0 HPUs\n"
          ]
        }
      ],
      "source": [
        "import wandb\n",
        "from pytorch_lightning.loggers import WandbLogger\n",
        "from pytorch_lightning.callbacks import Callback, EarlyStopping\n",
        "\n",
        "wandb.finish()\n",
        "wandb_logger = WandbLogger(project=\"Pix2Struct\", name=\"demo-run-pix2struct-adafactor\")\n",
        "\n",
        "trainer = pl.Trainer(\n",
        "        accelerator=\"gpu\",\n",
        "        devices=1,\n",
        "        max_steps=config.get(\"max_steps\"),\n",
        "        check_val_every_n_epoch=config.get(\"check_val_every_n_epoch\"),\n",
        "        gradient_clip_val=config.get(\"gradient_clip_val\"), # use gradient clipping\n",
        "        accumulate_grad_batches=config.get(\"accumulate_grad_batches\"), # use gradient accumulation\n",
        "        logger=wandb_logger,\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "qn_Y4JkoFZTj"
      },
      "outputs": [],
      "source": [
        "trainer.fit(pl_module)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jgoT-ebGhkJT"
      },
      "source": [
        "## Inference\n",
        "\n",
        "After training, you can load the model by getting the `pl_module.model` attribute from the PyTorch Ligthing module above (which returns the HuggingFace model). Then you can call either `save_pretrained` or `push_to_hub` on that object, and reload it back using `from_pretrained`. Refer to the [docs](https://huggingface.co/docs/transformers/main/en/model_doc/pix2struct#transformers.Pix2StructForConditionalGeneration) regarding inference code examples."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vgeDopcjLl1x"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "machine_shape": "hm",
      "provenance": [],
      "authorship_tag": "ABX9TyNdQrxsGhA+nYOIRIZGnndQ",
      "include_colab_link": true
    },
    "gpuClass": "standard",
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    },
    "widgets": {
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